Branch to Snowflake

This page provides you with instructions on how to extract data from Branch and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Branch?

Branch Metrics lets businesses generate deep links they can use to track conversions and user engagement on web and mobile transactions. It provides a business analytics dashboard to surface user behavior data.

What is Snowflake?

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.

Getting data out of Branch

Branch exposes data for things like install, open, clicks, and web session start through webhooks to user-defined HTTP POST callbacks. You can add a webhook through the Branch dashboard.

Sample Branch data

Branch exchanges data in JSON format. Here’s an example of the data returned for a clicks endpoint:

POST
User-agent: Branch Metrics API
Content-Type: application/json
{
    click_id: a unique identifier,
    event: 'click',
    event_timestamp: 'link click timestamp',
    os: 'iOS' | 'Android',
    os_version: 'the OS version',
    metadata: {
        ip: 'click IP',
        userAgent: 'click UA',
        browser: 'browser',
        browser_version: 'browser version',
        brand: 'phone brand',
        model: 'phone model',
        os: 'browser OS',
        os_version: 'OS version'
    },
    query: { any query parameters appended to the link },
    link_data: { link data dictionary - see below }
}

// link data dictionary example
{
    branch_id: 'unique identifier for unique link',
    date_ms: 'link creation date with millisecond',
    date_sec: 'link creation date with second',
    date: 'link creation date',
    domain: 'domain label',
    data: {
        +url: the Branch link,
        ... other deep link data
    },
    campaign: 'campaign label',
    feature: 'feature label',
    channel: 'channel label'
    tags: [tags array],
    stage: 'stage label',
}

Preparing Branch data

If you don’t already have a data structure in which to store the data you retrieve, you’ll have to create a schema for your data tables. Then, for each value in the response, you’ll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Branch's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you’ll likely have to create additional tables to capture the unpredictable cardinality in each record.

Preparing data for Snowflake

Depending on the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.

Note that you don't need to define a schema in advance when loading JSON data into Snowflake.

Loading data into Snowflake

The Snowflake documentation's Data Loading Overview section can help you with the task of loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are the limitations on that tool will make it a non-starter as a reliable ETL solution. Alternatively, there are two main steps for getting data into Snowflake:

  • Use the PUT command to stage files.
  • Use the COPY INTO table command to load prepared data into an awaiting table.

You’ll have the option of copying from your local drive or from Amazon S3. One of Snowflake's slick features lets you make a virtual warehouse that can power the insertion process.

Keeping Branch data up to date

Once you’ve set up the webhooks you want and have begun collecting data, you can relax – as long as everything continues to work correctly. You’ll have to keep an eye out for any changes to Branch’s webhooks implementation.

Other data warehouse options

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Branch data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.